Dual-energy-based metal segmentation for metal artifact reduction in dental computed tomography

Metal artifact reduction method based on single spectral CT (MARSS)

BACKGROUND

For patients with metal implants, computed tomography (CT) imaging results suffer from metal artifacts, which seriously affect image evaluation and even lead to misdiagnosis. Because spectral CT technology has the advantage of quantitative imaging, basis material decomposition, and so on, the current metal artifact reduction methods are utilizing spectral information to reduce metal artifacts with good results. However, they usually require projection data from multiple spectra or energy-windows, which is difficult to realize in conventional CT.

PURPOSE

To satisfy the status quo, the aim of this work is to propose a metal artifact reduction (MAR) method based on single spectral CT (MARSS). By incorporating prior information, the average density of some base materials, and a constrained image reconstruction model is established. It forces the solution spaces of the materials to be discrete and finite, making the model easier to solve.

METHODS

The MARSS method uses the idea of discrete tomography to establish a constrained reconstruction model. By incorporating priori knowledge, the constraint forces the solution spaces of some materials to be discrete, which can effectively downsize the solution space and reduce the ill-posedness of this problem. Then, an iteration algorithm is developed to solve this model. This algorithm iterates alternately between reconstruction and discretization. It ensures that the solution spaces are discrete while the polychromatic projection of the reconstructed image converges to that of the scanned object.

RESULTS

The MRASS method significantly reduces artifacts and restores structures near metal to a large extent. Unlike the comparison MAR methods, it effectively prevents the introduction of new artifacts and distortion of the structure.

CONCLUSIONS

The MARSS method can achieve MAR based on single spectral CT. Subjective and quantitative evaluation of the results show that the method significantly improves image quality compared to competing methods.

The current role and future potential of digital diagnostic imaging in implant dentistry: A scoping review

OBJECTIVES

Diagnostic imaging is crucial for implant dentistry. This review provides an up-to-date perspective on the application of digital diagnostic imaging in implant dentistry.

METHODS

Electronic searches were conducted in PubMed focusing on the question 'when (and why) do we need diagnostic imaging in implant dentistry?' The search results were summarised to identify different applications of digital diagnostic imaging in implant dentistry.

RESULTS

The most used imaging modalities in implant dentistry include intraoral periapical radiographs, panoramic views and cone beam computed tomography (CBCT). These are dependent on acquisition standardisation to optimise image quality. Particularly for CBCT, other technical parameters (i.e., tube current, tube voltage, field-of-view, voxel size) are relevant minimising the occurrence of artefacts. There is a growing interest in digital workflows, integrating diagnostic imaging and automation. Artificial intelligence (AI) has been incorporated into these workflows and is expected to play a significant role in the future of implant dentistry. Preliminary evidence supports the use of ionising-radiation-free imaging modalities (e.g., MRI and ultrasound) that can add value in terms of soft tissue visualisation.

CONCLUSIONS

Digital diagnostic imaging is the sine qua non in implant dentistry. Image acquisition protocols must be tailored to the patient's needs and clinical indication, considering the trade-off between radiation exposure and needed information. growing evidence supporting the benefits of digital workflows, from planning to execution, and the future of implant dentistry will likely involve a synergy between human expertise and AI-driven intelligence. Transiting into ionising-radiation-free imaging modalities is feasible, but these must be further developed before clinical implementation.

CT-Based Modeling of the Dentition for Craniomaxillofacial Surgical Planning

ABSTRACT

Historically, the accuracy of imaging teeth by computed tomography (CT) has been suboptimal and deemed inadequate for surgical planning of orthognathic procedures. However, recent advances in CT hardware and software have significantly improved the accuracy of imaging occlusal anatomy. This technical note describes a quantitative means of evaluating the accuracy of CT-based modeling of teeth. Three-dimensional models of the dentition were created from a CT scan obtained of a craniomaxillofacial skeleton. Multiple reconstruction algorithms and modeling parameters were applied. The dentition of the same skeleton was scanned using a handheld optical scanning device to serve as the "gold standard." Semi-automated registrations of CT and optically acquired models were performed and deviation analysis was conducted. On average, the deviation of the CT model with the optical scan measured 0.19 to 0.25 mm across the various reconstruction and modeling parameters, with a mean of 0.22 mm. Computed tomography underestimated contours at cusp tips, while overestimating contours in occlusal groves. The use of bone reconstruction algorithms and decreased model smoothing resulted in more accurate models, though greater surface noise. Future studies evaluating the clinical effectiveness of CT-based occlusal splints should take this finding into account.

Development and dosimetric assessment of an automatic dental artifact classification tool to guide artifact management techniques in a fully automated treatment planning workflow

Purpose:

We conducted our study to develop a tool capable of automatically detecting dental artifacts in a CT scan on a slice-by-slice basis and to assess the dosimetric impact of implementing the tool into the Radiation Planning Assistant (RPA), a web-based platform designed to fully automate the radiation therapy treatment planning process.

Methods:

We developed an automatic dental artifact identification tool and assessed the dosimetric impact of its use in the RPA. Three users manually annotated 83,676 head-and-neck (HN) CT slices (549 patients). Majority-voting was applied to the individual annotations to determine the presence or absence of dental artifacts. The patients were divided into train, cross-validation, and test data sets (ratio: 3:1:1, respectively). A random subset of images without dental artifacts was used to balance classes (1:1) in the training data set. The Inception-V3 deep learning model was trained with the binary cross-entropy loss function. With use of this model, we automatically identified artifacts on 15 RPA HN plans on a slice-by-slice basis and investigated three dental artifact management methods applied before and after volumetric modulated arc therapy (VMAT) plan optimization. The resulting dose distributions and target coverage were quantified.

Results:

Per-slice accuracy, sensitivity, and specificity were 99 %, 91 %, and 99 %, respectively. The model identified all patients with artifacts. Small dosimetric differences in total plan dose were observed between the various density-override methods (±1 Gy). For the pre- and post-optimized plans, 90 % and 99 %, respectively, of dose comparisons resulted in normal structure dose differences of ±1 Gy. Differences in the volume of structures receiving 95 % of the prescribed dose (V95[%]) were ≤0.25 % for 100 % of plans.

Conclusion:

The dosimetric impact of applying dental artifact management before and after artifact plan optimization was minor. Our results suggest that not accounting for dental artifacts in the current RPA workflow (where only post-optimization dental artifact management is possible) may result in minor dosimetric differences. If RPA users choose to override CT densities as a solution to managing dental artifacts, our results suggest segmenting the volume of the artifact and overriding its density to water is a safe option.

3D X-ray micro-computed tomography imaging for the microarchitecture evaluation of porous metallic implants and scaffolds

As an advanced microscopy technology with strong sample adaptability and non-destructive three-dimensional (3D) characteristics, X-ray micro-computed tomography (Micro-CT) can establish the overall connection between various microarchitecture parameters and accelerate the research process of porous metallic implants and scaffolds. In this review, the Micro-CT based quantitative evaluation methods of microarchitecture and bone formation are investigated. To ensure reliability of the results, the Micro-CT setup is discussed briefly and the essential image processing algorithms are introduced in detail. The significance and limitations of Micro-CT are analyzed in the context of research on porous metallic implants. We also discuss the future development of Micro-CT technology in the field of biological tissue engineering.

Evaluation of dual energy CT and iterative metal artefact reduction (iMAR) for artefact reduction in radiation therapy

Metal artefacts pose a common problem in single energy computed tomography (SECT) images used for radiotherapy. Virtual monoenergetic (VME) images constructed with dual energy computed tomography (DECT) scans can be used to reduce beam hardening artefacts. Dual energy metal artefact reduction is compared and combined with iterative metal artefact reduction (iMAR) to determine optimal imaging strategies for patients with metal prostheses. SECT and DECT scans were performed on a Siemens Somatom AS-64 Slice CT scanner. Images were acquired of a modified CIRS pelvis phantom with 6, 12, 20 mm diameter stainless steel rods and VME images reconstructed at 100, 120, 140 and 190 keV. These were post-reconstructed with and without the iMAR algorithm. Artefact reduction was measured using: (1) the change in Hounsfield Unit (HU) with and without metal artefact reduction (MAR) for 4 regions of interest; (2) the total number of artefact pixels, defined as pixels with a difference (between images with metal rod and without) exceeding a threshold; (3) the difference in the mean pixel intensity of the artefact pixels. DECT, SECT + iMAR and DECT + iMAR were compared. Both SECT + iMAR and DECT + iMAR offer successful MAR for phantom simulating unilateral hip prosthesis. DECT gives minimal artefact reduction over iMAR alone. Quantitative metrics are advantageous for MAR analysis but have limitations that leave room for metric development.

Development of a denoising convolutional neural network-based algorithm for metal artifact reduction in digital tomosynthesis for arthroplasty: A phantom study

The present study aimed to develop a denoising convolutional neural network metal artifact reduction hybrid reconstruction (DnCNN-MARHR) algorithm for decreasing metal objects in digital tomosynthesis (DT) for arthroplasty by using projection data. For metal artifact reduction (MAR), we implemented a DnCNN-MARHR algorithm based on a training network (mini-batch stochastic gradient descent algorithm with momentum) to estimate the residual reference (140 keV virtual monochromatic [VM]) and object (70 kV with metal artifacts) images. For this, we used projection data and subtracted the estimated residual images from the object images, involving hybrid and subjectively reconstructed image usage (back projection and maximum likelihood expectation maximization [MLEM]). The DnCNN-MARHR algorithm was compared with the dual-energy material decomposition reconstruction algorithm (DEMDRA), VM, MLEM, established and commonly used filtered back projection (FBP), and a simultaneous algebraic reconstruction technique-total variation (SART-TV) with MAR processing. MAR was compared using artifact index (AI) and texture analysis. Artifact spread functions (ASFs) for images that were out-of-plane and in-focus were evaluated using a prosthesis phantom. The overall performance of the DnCNN-MARHR algorithm was adequate with regard to the ASF, and the derived images showed better results, without being influenced by the metal type (AI was almost equal to the best value for the DEMDRA). In the ASF analysis, the DnCNN-MARHR algorithm generated better MAR compared with that obtained employing usual algorithms for reconstruction using MAR processing. In addition, comparison of the difference (mean square error) between DnCNN-MARHR and the conventional algorithm resulted in the smallest VM. The DnCNN-MARHR algorithm showed the best performance with regard to image homogeneity in the texture analysis. The proposed algorithm is particularly useful for reducing artifacts in the longitudinal direction, and it is not affected by tissue misclassification.

U-net based metal segmentation on projection domain for metal artifact reduction in dental CT

Unlike medical computed tomography (CT), dental CT often suffers from severe metal artifacts stemming from high-density materials employed for dental prostheses. Despite the many metal artifact reduction (MAR) methods available for medical CT, those methods do not sufficiently reduce metal artifacts in dental CT images because MAR performance is often compromised by the enamel layer of teeth, whose X-ray attenuation coefficient is not so different from that of prosthetic materials. We propose a deep learning-based metal segmentation method on the projection domain to improve MAR performance in dental CT. We adopted a simplified U-net for metal segmentation on the projection domain without using any information from the metal-artifacts-corrupted CT images. After training the network with the projection data of five patients, we segmented the metal objects on the projection data of other patients using the trained network parameters. With the segmentation results, we corrected the projection data by applying region filling inside the segmented region. We fused two CT images, one from the corrected projection data and the other from the original raw projection data, and then we forward-projected the fused CT image to get the fused projection data. To get the final corrected projection data, we replaced the metal regions in the original projection data with the ones in the fused projection data. To evaluate the efficacy of the proposed segmentation method on MAR, we compared the MAR performance of the proposed segmentation method with a conventional MAR method based on metal segmentation on the CT image domain. For the MAR performance evaluation, we considered the three primary MAR performance metrics: the relative error (REL), the sum of square difference (SSD), and the normalized absolute difference (NAD). The proposed segmentation method improved MAR performances by around 5.7% for REL, 6.8% for SSD, and 8.2% for NAD. The proposed metal segmentation method on the projection domain showed better MAR performance than the conventional segmentation on the CT image domain. We expect that the proposed segmentation method can improve the performance of the existing MAR methods that are based on metal segmentation on the CT image domain.